Implementing PMU-based Systems for Transmission and ...
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Implementing PMU-based Systems for Transmission and Distribution System Analysis Marianna Vaiman, V&R Energy and Mohd Khairun Nizam Mohd Sarmin, TNB
Transcript of Implementing PMU-based Systems for Transmission and ...
Implementing PMU-based Systems for Transmission and Distribution System Analysis
Marianna Vaiman, V&R Energyand
Mohd Khairun Nizam Mohd Sarmin, TNB
Contents
1. TNB’s Experience Deploying a PMU-Based EMS System
Mohd Khairun Nizam Mohd Sarmin, TNB and Marianna Vaiman, V&R Energy
2. Real-Time PMU-based Distribution System Monitoring Platform
Marianna Vaiman, V&R Energy
1. TNB’s Experience Deploying a PMU-Based EMS System
Mohd Khairun Nizam Mohd Sarmin, Head of Power System Unit at TNB Research
andMarianna Vaiman, CEO at V&R Energy
Background• Tenaga Nasional Berhad (TNB) is the largest electricity utility in Malaysia serving 9.2
million customers– TNB owns 57.2% of the 26.296 MW generation installed capacity, and operates transmission network
with a total circuit length of 23,082 km and distribution network with a total circuit length of 660,038 km
– TNB has recorded the highest demand of 18,566 MW and the highest energy demand of 391.85 GWh/day
• TNB Research has been instrumental in implementing the Wide-Area Intelligent System program at TNB which explores the potential benefits of PMU
– Several real-time PMU-based applications have been successfully developed
• TNB aims for full network observability with PMUs for voltage levels of 500 kV and 275 kV by 2021
PMU-Based EMS System
• TNB PMU ROSE is PMU-based EMS system developed by V&R Energy
• Consists of multiple integrated applications: – LSE POM Server
– POM-Cascading Analysis application
– POM-OPM remedial actions application
– TNB PMU Viewer
– TNB Cascading Viewer
- LSE Linear State Estimation;
- LSE RAW PMU-based State Estimator cases in Siemens PTI PSS/E .raw data format;
- POM Physical and Operational Margins Suite;
- PMU Phasor Measurement Unit streaming data;
- RAW Conventional State Estimator (CSE) cases;
- LSE PMU Conditioned and expanded PMU streaming data
- RAW PMU Voltage magnitude and phase from CSE cases
LSE POM Server Analysis Framework
• Multi-step process:– Bad data detection– Event detection– Filtering and smoothing– LSE (e.g, weighted least
squares method)– Creation of expanded and
conditioned PMU data stream– Creation of PMU-based State
Estimator cases (e.g., LSE Case)
TNB PMU ROSE System Facts
• The conventional State Estimator (CSE) cases represent TNB system operational model with over 1,500 buses/2,800 branches after topology processing
• Signals from 1218 PMUs / 7 PDCs are sent to TNB PMU ROSE at the rate of 25 samples/second
• Linear state estimation and all related computations are performed at the rate of 25 samples/second
• LSE cases are created at a user-defined intervals, when bad data is detected for longer then a certain interval, or an Event is detected by TNB PMU ROSE
Testing and Validation of TNB PMU ROSE
• The conventional State Estimator (CSE) and the PMU are emulated using real-time data from the real-time simulator of OPAL-RT
• TNB PMU ROSE is run on a dedicated server
• Control signals are sent to the real-time simulator for remedial action
• The results show that LSE (dark green line) successfully suppress the error and provides voltage estimates with a difference of less than 0.01% compared to the true value
• Substation with multiple PMU measurements with random errors and noise denoted by yellow, light green and red lines
• The true value is blue line
Estimating Values Using LSE
POM – Cascading Analysis Application
• Performs automated cascading analysis using LSE Cases created by the LSE POM
• Cascading outages are simulated based on consecutive AC contingency analysis
• Two cascading scenarios are incorporated: – “Thermal” scenario
“Voltage” scenario
• Cascading analysis may be performed with and without remedial actions
Cascading Analysis Framework
• There are three approaches to use OPM (Optimal Mitigation Measures) remedial actions during execution of cascading analysis: – (1) Determine remedial actions after an
Initiating Event (IE); – (2) Determine remedial actions at the last
tier (e.g., at the end of cascading process); – (3) Determine remedial actions after an IE
and at the last tier.
• Cascading Viewer visualizes results of online cascading analysis
• 1877 N-1 initiating events are analyzed in one run
• 41 critical cascading events were identified and ranked based on severity measured using the performance index
Cascading Analysis
• A critical Initiating event results in stability violation
• The initiating event was tripping a transformer which leads to overload on other branches
Stability Violation as a Result of Cascading
Voltage Unstable after Cascading Event
• Optimal mitigation measures are identified to alleviate this stability violation
• The effect of these measures can be seen in the PMU Viewer
Effect of Remedial Actions
Voltage Stable after Remedial Actions
Conclusions and Future Work• PMU-based EMS system was successfully deployed at TNB• The LSE increases network visibility with accurate state estimates derived
based on a set of observable PMU measurements:– PMU measurements are smoothed and bad data are filtered, and bus fault and line
trip events are detected
• The online cascading analysis application has been demonstrated as the use case of LSE utilizing the PMU-based state estimator case – Fast AC contingency analysis solved with the full Newton method
• Remedial actions were demonstrated to be effective in alleviating thermal, voltage and stability violations
• Future work will involve integration with production SCADA/EMS system and PMUs, and exploring other potential applications that will benefit from the LSE
2. Real-Time PMU-based Distribution System Monitoring Platform
Marianna Vaiman, CEO at V&R Energy
Real-Time Distribution System Monitoring Platform• Provides real-time situational awareness in order to
improve resilience of the distribution grid and enhance its reliability
• D-PMU ROSE platform consists of the following functionalities:– Three-phase distribution linear state estimation (D-LSE)
– Bad PMU data detection and correction
– Observability analysis for real-time situational awareness
– Identifying switching events
– Archiving and alarming
– Advanced visualization of distribution grid state
– Optimal PMU placement for full distribution grid observability
– Validating model and PMU measurements
Components of D-LSE Framework
• Multi-step process:1. Bad data detection, correction, alarming, reporting2. Combination of filtering and smoothing techniques 3. Observability analysis4. Three-phase Distribution Linear State Estimation 5. Detection of switching events (only based on PMU
data)6. Real-time system monitoring (voltage and thermal)7. Visualization, archiving
• Machine learning is used to improve accuracy of event detection in real-time
• Step 1: Data pre-screening
• Step 2: Filtering & smoothing
• Step 3: LSE
– Considers relationship between signals
– Based on WLS method
Bad Data Detection and Conditioning
Bad Data Detection and Conditioning
Raw PMU values (phase B)
Estimated values after the D-LSE (filtering and weighted least squares method): Yellow
Source: ComEd
Observability Analysis and PMU Placement
• Purpose of observability analysis is to identify portions of the network observable with existing PMUs– Performed in real-time
– Generates observability reports
• Purpose of optimal PMU placement is to determine locations of PMU installations to achieve full system observability– Off-line calculation
– Creates PMU placement file
D-LSE Result –Observability
Analysis & Visualization
D-PMU ROSE considers a
power system network to
be observable for a given
network topology if voltage
vector at each node can be
calculated based on the
PMU measurements
Blue – nodes and branches
that are observable with
planned PMU installations
(for current network
topology)
Black – non-observable
nodes and branches
– PMU installations
Source: ComEd
Detecting and Alarming on Switching Events
Source: ComEd
Topology Change
• Estimated value follow raw data during both steady state and transient conditions
• D-LSE identifies defined topology correctly
Source: ComEd
Alarming
• Event Alarm– If a switching event is identified, the Event Alarm indicator turns red
• Bad Data Alarm– If bad data is identified, Bad Data alarm turns red
• Violation Alarm– If a violation of voltage and/or thermal limit occurs, the Violation Alarm
indicator turns red
• Multiple types of alarms might be issued simultaneously
Use of the Platform for Grid Modernization Applications
• The platform consists of the following applications:– Hybrid State Estimator that uses both PMU and SCADA data– Advanced applications:
• FLISR (Fault Location, Isolation and Service Restoration) enhancement • Volt/Var optimization• DER dispatch
• NYSERDA demonstration project with Quanta Technology and Central Hudson
